OpenRaiser/Intern-Atlas huggingface dataset:
Fuzzy Hashing as Perturbation-Consistent Adversarial Kernel Embedding
Venue: arXiv cs.LG 2018
Year: 2018

Measuring the similarity of two files is an important task in malware analysis, with fuzzy hash functions being a popular approach. Traditional fuzzy hash functions are data agnostic: they do not learn from a particular dataset how to determine similarity; their behavior is fixed across all datasets. In this paper, we demonstrate that fuzzy hash functions can be learned in a novel minimax training framework and that these learned fuzzy hash functions outperform traditional fuzzy hash functions at the file similarity task for Portable Executable files. In our approach, hash digests can be extracted from the kernel embeddings of two kernel networks, trained in a minimax framework, where the roles of players during training (i.e adversary versus generator) alternate along with the input data. We refer to this new minimax architecture as perturbation-consistent. The similarity score for a pair of files is the utility of the minimax game in equilibrium. Our experiments show that learned fuzzy hash functions generalize well, capable of determining that two files are similar even when one of those files was generated using insertion and deletion operations.
Rethinking Epistemic Logic with Belief Bases
Venue: arXiv cs.AI 2018
Year: 2018

We introduce a new semantics for a logic of explicit and implicit beliefs based on the concept of multi-agent belief base. Differently from existing Kripke-style semantics for epistemic logic in which the notions of possible world and doxastic/epistemic alternative are primitive, in our semantics they are non-primitive but are defined from the concept of belief base. We provide a complete axiomatization and prove decidability for our logic via a finite model argument. We also provide a polynomial embedding of our logic into Fagin & Halpern's logic of general awareness and establish a complexity result for our logic via the embedding.
Interval type-2 Beta Fuzzy Near set based approach to content based image retrieval
Venue: arXiv cs.CV 2018
Year: 2018

In an automated search system, similarity is a key concept in solving a human task. Indeed, human process is usually a natural categorization that underlies many natural abilities such as image recovery, language comprehension, decision making, or pattern recognition. In the image search axis, there are several ways to measure the similarity between images in an image database, to a query image. Image search by content is based on the similarity of the visual characteristics of the images. The distance function used to evaluate the similarity between images depends on the criteria of the search but also on the representation of the characteristics of the image; this is the main idea of the near and fuzzy sets approaches. In this article, we introduce a new category of beta type-2 fuzzy sets for the description of image characteristics as well as the near sets approach for image recovery. Finally, we illustrate our work with examples of image recovery problems used in the real world.
Composing Text and Image for Image Retrieval - An Empirical Odyssey
Venue: arXiv cs.CV 2018
Year: 2018

In this paper, we study the task of image retrieval, where the input query is specified in the form of an image plus some text that describes desired modifications to the input image. For example, we may present an image of the Eiffel tower, and ask the system to find images which are visually similar but are modified in small ways, such as being taken at nighttime instead of during the day. To tackle this task, we learn a similarity metric between a target image and a source image plus source text, an embedding and composing function such that target image feature is close to the source image plus text composition feature. We propose a new way to combine image and text using such function that is designed for the retrieval task. We show this outperforms existing approaches on 3 different datasets, namely Fashion-200k, MIT-States and a new synthetic dataset we create based on CLEVR. We also show that our approach can be used to classify input queries, in addition to image retrieval.
Does the Position of Surgical Service Providers in Intra-Operative Networks Matter? Analyzing the Impact of Influencing Factors on Patients' Outcome
Venue: arXiv cs.SI 2018
Year: 2018

We analyzed the relation between surgical service providers' network structure and surgical team size with patient outcome during the operation. We did correlation analysis to evaluate the associations among the network structure measures in the intra-operative networks of surgical service providers. We focused on intra-operative networks of surgical service providers, in a quaternary-care academic medical center, using retrospective Electronic Medical Record (EMR) data. We used de-identified intra-operative data for adult patients who received nonambulatory/nonobstetric surgery in a main operating room at Shands at the University of Florida between June 1, 2011 and November 1, 2014. The intra-operative dataset contained 30,211 unique surgical cases. To perform the analysis, we created the networks of surgical service providers and calculated several network structure measures at both team and individual levels. We considered number of patients' complications as the target variable and assessed its interrelations with the calculated network measures along with other influencing factors (e.g. surgical team size, type of surgery). Our results confirm the significant role of interactions among surgical providers on patient outcome. In addition, we observed that highly central providers at the global network level are more likely to be associated with a lower number of surgical complications, while locally important providers might be associated with higher number of complications. We also found a positive relation between age of patients and number of complications.
The Prefetch Aggressiveness Tradeoff in 360$^{\circ}$ Video Streaming
Venue: arXiv cs.MM 2018
Year: 2018

With 360$^{\circ}$ video, only a limited fraction of the full view is displayed at each point in time. This has prompted the design of streaming delivery techniques that allow alternative playback qualities to be delivered for each candidate viewing direction. However, while prefetching based on the user's expected viewing direction is best done close to playback deadlines, large buffers are needed to protect against shortfalls in future available bandwidth. This results in conflicting goals and an important prefetch aggressiveness tradeoff problem regarding how far ahead in time from the current playpoint prefetching should be done. This paper presents the first characterization of this tradeoff. The main contributions include an empirical characterization of head movement behavior based on data from viewing sessions of four different categories of 360$^{\circ}$ video, an optimization-based comparison of the prefetch aggressiveness tradeoffs seen for these video categories, and a data-driven discussion of further optimizations, which include a novel system design that allows both tradeoff objectives to be targeted simultaneously. By qualitatively and quantitatively analyzing the above tradeoffs, we provide insights into how to best design tomorrow's delivery systems for 360$^{\circ}$ videos, allowing content providers to reduce bandwidth costs and improve users' playback experiences.
Exploiting Full/Half-Duplex User Relaying in NOMA Systems
Venue: arXiv cs.IT 2018
Year: 2018

In this paper, a novel cooperative non-orthogonal multiple access (NOMA) system is proposed, where one near user is employed as decode-and-forward (DF) relaying switching between full-duplex (FD) and half-duplex (HD) mode to help a far user. Two representative cooperative relaying scenarios are investigated insightfully. The \emph{first scenario} is that no direct link exists between the base station (BS) and far user. The \emph{second scenario} is that the direct link exists between the BS and far user. To characterize the performance of potential gains brought by FD NOMA in two considered scenarios, three performance metrics outage probability, ergodic rate and energy efficiency are discussed. More particularly, we derive new closed-form expressions for both exact and asymptotic outage probabilities as well as delay-limited throughput for two NOMA users. Based on the derived results, the diversity orders achieved by users are obtained. We confirm that the use of direct link overcomes zero diversity order of far NOMA user inherent to FD relaying. Additionally, we derive new closed-form expressions for asymptotic ergodic rates. Based on these, the high signal-to-noise radio (SNR) slopes of two users for FD NOMA are obtained. Simulation results demonstrate that: 1) FD NOMA is superior to HD NOMA in terms of outage probability and ergodic sum rate in the low SNR region; and 2) In delay-limited transmission mode, FD NOMA has higher energy efficiency than HD NOMA in the low SNR region; However, in delay-tolerant transmission mode, the system energy efficiency of HD NOMA exceeds FD NOMA in the high SNR region.
Distill-Net: Application-Specific Distillation of Deep Convolutional Neural Networks for Resource-Constrained IoT Platforms
Venue: arXiv cs.LG 2018
Year: 2018

Many Internet-of-Things (IoT) applications demand fast and accurate understanding of a few key events in their surrounding environment. Deep Convolutional Neural Networks (CNNs) have emerged as an effective approach to understand speech, images, and similar high dimensional data types. Algorithmic performance of modern CNNs, however, fundamentally relies on learning class-agnostic hierarchical features that only exist in comprehensive training datasets with many classes. As a result, fast inference using CNNs trained on such datasets is prohibitive for most resource-constrained IoT platforms. To bridge this gap, we present a principled and practical methodology for distilling a complex modern CNN that is trained to effectively recognize many different classes of input data into an application-dependent essential core that not only recognizes the few classes of interest to the application accurately, but also runs efficiently on platforms with limited resources. Experimental results confirm that our approach strikes a favorable balance between classification accuracy (application constraint), inference efficiency (platform constraint), and productive development of new applications (business constraint).
Decentralized Computation Offloading for Multi-User Mobile Edge Computing: A Deep Reinforcement Learning Approach
Venue: arXiv cs.LG 2018
Year: 2018

Mobile edge computing (MEC) emerges recently as a promising solution to relieve resource-limited mobile devices from computation-intensive tasks, which enables devices to offload workloads to nearby MEC servers and improve the quality of computation experience. Nevertheless, by considering a MEC system consisting of multiple mobile users with stochastic task arrivals and wireless channels in this paper, the design of computation offloading policies is challenging to minimize the long-term average computation cost in terms of power consumption and buffering delay. A deep reinforcement learning (DRL) based decentralized dynamic computation offloading strategy is investigated to build a scalable MEC system with limited feedback. Specifically, a continuous action space-based DRL approach named deep deterministic policy gradient (DDPG) is adopted to learn efficient computation offloading policies independently at each mobile user. Thus, powers of both local execution and task offloading can be adaptively allocated by the learned policies from each user's local observation of the MEC system. Numerical results are illustrated to demonstrate that efficient policies can be learned at each user, and performance of the proposed DDPG based decentralized strategy outperforms the conventional deep Q-network (DQN) based discrete power control strategy and some other greedy strategies with reduced computation cost. Besides, the power-delay tradeoff is also analyzed for both the DDPG based and DQN based strategies.
Impact of Tool Support in Patch Construction
Venue: arXiv cs.SE 2018
Year: 2018

In this work, we investigate the practice of patch construction in the Linux kernel development, focusing on the differences between three patching processes: (1) patches crafted entirely manually to fix bugs, (2) those that are derived from warnings of bug detection tools, and (3) those that are automatically generated based on fix patterns. With this study, we provide to the research community concrete insights on the practice of patching as well as how the development community is currently embracing research and commercial patching tools to improve productivity in repair. The result of our study shows that tool-supported patches are increasingly adopted by the developer community while manually-written patches are accepted more quickly. Patch application tools enable developers to remain committed to contributing patches to the code base. Our findings also include that, in actual development processes, patches generally implement several change operations spread over the code, even for patches fixing warnings by bug detection tools. Finally, this study has shown that there is an opportunity to directly leverage the output of bug detection tools to readily generate patches that are appropriate for fixing the problem, and that are consistent with manually-written patches.
A cortical-inspired model for orientation-dependent contrast perception: a link with Wilson-Cowan equations
Venue: arXiv cs.CV 2018
Year: 2018

We consider a differential model describing neuro-physiological contrast perception phenomena induced by surrounding orientations. The mathematical formulation relies on a cortical-inspired modelling [10] largely used over the last years to describe neuron interactions in the primary visual cortex (V1) and applied to several image processing problems [12,19,13]. Our model connects to Wilson-Cowan-type equations [23] and it is analogous to the one used in [3,2,14] to describe assimilation and contrast phenomena, the main novelty being its explicit dependence on local image orientation. To confirm the validity of the model, we report some numerical tests showing its ability to explain orientation-dependent phenomena (such as grating induction) and geometric-optical illusions [21,16] classically explained only by filtering-based techniques [6,18].
FDSNet: Finger dorsal image spoof detection network using light field camera
Venue: arXiv cs.CV 2018
Year: 2018

At present spoofing attacks via which biometric system is potentially vulnerable against a fake biometric characteristic, introduces a great challenge to recognition performance. Despite the availability of a broad range of presentation attack detection (PAD) or liveness detection algorithms, fingerprint sensors are vulnerable to spoofing via fake fingers. In such situations, finger dorsal images can be thought of as an alternative which can be captured without much user cooperation and are more appropriate for outdoor security applications. In this paper, we present a first feasibility study of spoofing attack scenarios on finger dorsal authentication system, which include four types of presentation attacks such as printed paper, wrapped printed paper, scan and mobile. This study also presents a CNN based spoofing attack detection method which employ state-of-the-art deep learning techniques along with transfer learning mechanism. We have collected 196 finger dorsal real images from 33 subjects, captured with a Lytro camera and also created a set of 784 finger dorsal spoofing images. Extensive experimental results have been performed that demonstrates the superiority of the proposed approach for various spoofing attacks.
GP-CNAS: Convolutional Neural Network Architecture Search with Genetic Programming
Venue: arXiv cs.NE 2018
Year: 2018

Convolutional neural networks (CNNs) are effective at solving difficult problems like visual recognition, speech recognition and natural language processing. However, performance gain comes at the cost of laborious trial-and-error in designing deeper CNN architectures. In this paper, a genetic programming (GP) framework for convolutional neural network architecture search, abbreviated as GP-CNAS, is proposed to automatically search for optimal CNN architectures. GP-CNAS encodes CNNs as trees where leaf nodes (GP terminals) are selected residual blocks and non-leaf nodes (GP functions) specify the block assembling procedure. Our tree-based representation enables easy design and flexible implementation of genetic operators. Specifically, we design a dynamic crossover operator that strikes a balance between exploration and exploitation, which emphasizes CNN complexity at early stage and CNN diversity at later stage. Therefore, the desired CNN architecture with balanced depth and width can be found within limited trials. Moreover, our GP-CNAS framework is highly compatible with other manually-designed and NAS-generated block types as well. Experimental results on the CIFAR-10 dataset show that GP-CNAS is competitive among the state-of-the-art automatic and semi-automatic NAS algorithms.
wav2letter++: The Fastest Open-source Speech Recognition System
Venue: arXiv cs.CL 2018
Year: 2018

This paper introduces wav2letter++, the fastest open-source deep learning speech recognition framework. wav2letter++ is written entirely in C++, and uses the ArrayFire tensor library for maximum efficiency. Here we explain the architecture and design of the wav2letter++ system and compare it to other major open-source speech recognition systems. In some cases wav2letter++ is more than 2x faster than other optimized frameworks for training end-to-end neural networks for speech recognition. We also show that wav2letter++'s training times scale linearly to 64 GPUs, the highest we tested, for models with 100 million parameters. High-performance frameworks enable fast iteration, which is often a crucial factor in successful research and model tuning on new datasets and tasks.
Clustering-Oriented Representation Learning with Attractive-Repulsive Loss
Venue: arXiv cs.LG 2018
Year: 2018

The standard loss function used to train neural network classifiers, categorical cross-entropy (CCE), seeks to maximize accuracy on the training data; building useful representations is not a necessary byproduct of this objective. In this work, we propose clustering-oriented representation learning (COREL) as an alternative to CCE in the context of a generalized attractive-repulsive loss framework. COREL has the consequence of building latent representations that collectively exhibit the quality of natural clustering within the latent space of the final hidden layer, according to a predefined similarity function. Despite being simple to implement, COREL variants outperform or perform equivalently to CCE in a variety of scenarios, including image and news article classification using both feed-forward and convolutional neural networks. Analysis of the latent spaces created with different similarity functions facilitates insights on the different use cases COREL variants can satisfy, where the Cosine-COREL variant makes a consistently clusterable latent space, while Gaussian-COREL consistently obtains better classification accuracy than CCE.
Fault Diagnosis for Distributed Systems using Accuracy Technique
Venue: arXiv cs.DC 2018
Year: 2018

Distributed Systems involve two or more computer systems which may be situated at geographically distinct locations and are connected by a communication network. Due to failures in the communication link, faults arise which may make the entire system dysfunctional. To enable seamless operation of the distributed system, these faults need to be detected and located accurately. This paper examines various techniques of handling faults in distributed systems and proposes and innovative technique which uses percent accuracy for detecting faulty nodes in the system. Every node in the system acts as an initiator and votes for certifying faulty nodes in the system. This certification is done on the basis of percent accuracy value of each faulty node which should exceed a predefined threshold value to qualify node as faulty. As the threshold increases, the number of faulty nodes detected in the system reduces. This is a decentralized approach with no dependency on a single node to act as a leader for diagnosis. This technique is also applicable to ad-hoc networks, which are static in nature.
AdaptMemBench: Application-Specific MemorySubsystem Benchmarking
Venue: arXiv cs.PF 2018
Year: 2018

Optimizing scientific applications to take full advan-tage of modern memory subsystems is a continual challenge forapplication and compiler developers. Factors beyond working setsize affect performance. A benchmark framework that exploresthe performance in an application-specific manner is essential tocharacterize memory performance and at the same time informmemory-efficient coding practices. We present AdaptMemBench,a configurable benchmark framework that measures achievedmemory performance by emulating application-specific accesspatterns with a set of kernel-independent driver templates. Thisframework can explore the performance characteristics of a widerange of access patterns and can be used as a testbed for potentialoptimizations due to the flexibility of polyhedral code generation.We demonstrate the effectiveness of AdaptMemBench with casestudies on commonly used computational kernels such as triadand multidimensional stencil patterns.
Gender differences in research productivity: a bibliometric analysis of the Italian academic system
Venue: arXiv cs.DL 2018
Year: 2018

The literature dedicated to analysis of the difference in research productivity between the sexes tends to agree in indicating better performance for men. This study enters in the vein of work on the subject. Through bibliometric examination of the entire population of research personnel working in the scientific-technological disciplines of Italian university system, it confirms the presence of significant differences in productivity between men and women. But such differences result as being smaller than reported in a large part of the literature, confirming an ongoing tendency towards decline, and are also seen as more noticeable for quantitative performance indicators than other indicators. The gap between the sexes presents important sectorial specificities. In spite of the generally better performance of men, it can not be ignored that there are a significant number of scientific sectors in which the performance of women does not result as inferior.
Simple Semi-Grant-Free Transmission Strategies Assisted by Non-Orthogonal Multiple Access
Venue: arXiv cs.IT 2018
Year: 2018

Grant-free transmission is an important feature to be supported by future wireless networks since it reduces the signalling overhead caused by conventional grant-based schemes. However, for grant-free transmission, the number of users admitted to the same channel is not caped, which can lead to a failure of multi-user detection. This paper proposes non-orthogonal multiple-access (NOMA) assisted semi-grant-free (SGF) transmission, which is a compromise between grant-free and grant-based schemes. In particular, instead of reserving channels either for grant-based users or grant-free users, we focus on an SGF communication scenario, where users are admitted to the same channel via a combination of grant-based and grant-free protocols. As a result, a channel reserved by a grant-based user can be shared by grant-free users, which improves both connectivity and spectral efficiency. Two NOMA assisted SGF contention control mechanisms are developed to ensure that, with a small amount of signalling overhead, the number of admitted grant-free users is carefully controlled and the interference from the grant-free users to the grant-based users is effectively suppressed. Analytical results are provided to demonstrate that the two proposed SGF mechanisms employing different successive interference cancelation decoding orders are applicable to different practical network scenarios.